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Comparative analysis of alkali-treated natural fibres for improved interfacial adhesion in composite materials

Interactions 2024年第1期245卷 206-206页

作者： Bhagat, Madhuri S. Jadhav, Varsha D. Kulkarni, Sumant Khanderao Satishkumar, P. Saminathan, Rajasekaran Department of Civil Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Natural fibers such as bamboo, kenaf, flax, ramie and hemp were chemically treated to improve their adherence with hydrophobic matrices. One common chemical process for altering the surface of these natural fibers is alkali treatment (AT). The experiment involves soaking bamboo fibers (BF) in solutions of 1, 10, and 30 wt % sodium hydroxide (NaOH) for one day, half an hour, and twenty minutes at room temperature. These alkali-treated and untreated bamboo fibers was compared with their physical and mechanical qualities. Using scanning electron microscopy (SEM) and the circular fiber assumption, the CSA of bamboo fibers both before and after AT were compared. Improving the AT procedures for BF at room temperature using varying NaOH concentrations is the focus of this effort. Additionally, the tensile characteristics of both untreated and variously treated AT Bamboo fibres are examined using a two-parameter Weibull distribution. Instead of just showing the average tensile strength, probabilistic tensile strength has shown to be a more effective method. This study proves without a reasonable doubt that adding 1 wt % NaOH to bamboo fiber makes natural fiber reinforced composites mechanically better. According to the SEM, the CSA of bamboo fibers decreases by about 13.62–20.24% and about 18.42–22.63%, respectively, after alkali treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Crop Yield Prediction Using Deep Reinforcement Learning

Crop Yield Prediction Using Deep Reinforcement Learning

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Trends in Electrical, Electronics, computer Engineering Conference (TEECCON)

作者： Sudhakar Deivasigamani A Jaya Mabel Rani R Natchadalingam P Vijayakarthik G Bala Sendhil Kumar Pundru Chandra Shaker Reddy Scrum Master Digipulse Technologies Inc New Jersy USA Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India CSE-Data Science R.L.Jalappa Institute of Technology Bengaluru Karnataka India Computer Science and Engineering R.L.Jalappa Institute of Technology Bengaluru Karnataka India Master of Business Administration Sri Manakula Vinayagar Engineering College Puducherry India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models.

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Flow Stress Modeling using the Material Constitutive Model of Modified Zerilli-Armstrong 6

Flow Stress Modeling using the Material Constitutive Model o...

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2020 6th International Conference on Materials, Mechanical Engineering and Automation Technology, MMEAT 2020

作者： Yu, Hong Shao, Zhenzhen Ren, Facai School of Data and Computer Science Shandong Women's University Jinan China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai China

The high temperature compression tests of 2Cr13 martensitic stainless steel under the deformation conditions at the temperatures between 1000 C and 1150 C with strain rates between 0.01s-1 and 10s-1 were carried out by using the Gleeble simulator. A new material constitutive model of modified Zerilli-Armstrong to predict 2Cr13 martensitic stainless steel flow stress behavior was developed. © Published under licence by IOP Publishing Ltd.

关键词： Martensitic stainless steel

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Creep Life Prediction of Heat-resistant Steel using Kachanov Model 6

Creep Life Prediction of Heat-resistant Steel using Kachanov...

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2020 6th International Conference on Materials, Mechanical Engineering and Automation Technology, MMEAT 2020

The accurate prediction of creep life is very important for the safety of the equipment used at high temperature. A series of creep tests of modified 9Cr-1Mo heat-resistant steel were carried out by using creep specimens machined from the plate at the temperatures of 565 C with stress ranging from 200 to 250MPa. The creep life of modified 9Cr-1Mo heat-resistant steel was estimated using the Kachanov creep damage model. The results show that the Kachanov creep damage model can be used to predict the creep life well. © Published under licence by IOP Publishing Ltd.

关键词： Creep

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Autoencoder based Temporal Convolutional Network for Anomaly Detection in data Streams

Autoencoder based Temporal Convolutional Network for Anomaly...

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Intelligent Systems and Computational networks (ICISCN), International Conference on

作者： K. Tamilselvan Ramee RiadHwsein S. Meenakshi Sundaram K. S. Vigneshwaran Abhinaya S Department of Information Technology AVS Engineering College Salem India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Nowadays, the rapid growth of data stream from different sources has needed the development of effective Anomaly Detection (AD) methods. The existing unsupervised Deep Learning (DL) approaches learnt various patterns and relationships in data streams without labeled data. However, detecting anomalies in data streams led to significant challenges such as handling high-dimensional data and capturing temporal dependencies. Hence, this research proposes an Autoencoder based Temporal Convolutional Network (AE-TCN) for AD in data streams. Initially, the input data is collected from Numenta Anomaly Benchmark (NAB) repository and further preprocessed with Regression Imputation (RI) to handle missing values and logarithmic scaling to normalize the data respectively. After that, Robustly Optimized Bidirectional Encoder Representation from Transformer approach (RoBERTa) is employed to extract the semantic features. Finally, AE-TCN is introduced to identify the anomalies in data streams by extracting temporal features from reconstructed nominal patterns. From the results, the proposed AE-TCN established high results in terms of Accuracy (Acc), F1-score, and Area Under Curve (AUC) of 99.89%, 94.06%, and 99.85% respectively when compared with existing Deep Autoencoder Neural Network with Adamax Optimization (DANN-AO).

关键词： Autoencoders Semantics data preprocessing Feature extraction Transformers Imputation Real-time systems Convolutional neural networks Anomaly detection Optimization

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Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

arXiv

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arXiv 2024年

作者： Katharria, Aashu Rajwar, Kanchan Pant, Millie Velásquez, Juan D. Snášel, Václav Deep, Kusum Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Statistical Quality Control and Operations Research Unit Indian Statistical Institute Hyderabad India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Department of Industrial Engineering University of Chile Santiago Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mathematics Indian Institute of Technology Roorkee Roorkee India

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Analysis of Friction and Dry Sliding Wear Characteristics of Nano-Aluminium Metal Composite Using Machine Learning Through Linear Regression Algorithm

Analysis of Friction and Dry Sliding Wear Characteristics of...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Syeda Jeelani Basri Kishore Kumar Mamidala N. Balakrishnan C. Geetha V.L. Kiranmai D S Jayalakshmi Department of Chemistry G.Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of MCA Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering RMK Engineering College Chennai Tamilnadu India Department of Physics KKR&KSR Institute of Technology and Sciences Guntur Andhra Pradesh India Department of Physics Sathyabama Institute of Science and Technology (Deemed to be University) Chennai Tamilnadu India

The aim of this research is to study the friction and dry sliding wear characteristics of nano-aluminium metal composite using machine learning through linear regression algorithm. Al 5182 alloy was used as a base material and SiC nano-particles were dispersed into the alloy through stir casting and ultra-sonic dispersion at different compositions (0.2, 0.4, 0.6, 0.8, and 1%) in order to analyze the effect of composition on friction and wear characteristics. L 25 alloy was used as a counter material in the experiments. The response variables, wear loss (Wl), were measured and the input parameters, composition, rotational speed, and sliding distance, were varied in the experiments. Signal to noise ratio was used to determine the best grouping of parameters that reduced wear loss. The results were then predicted using artificial neural network (ANN) and regression analysis. The derived equation for predicting the responses showed better accuracy in predicting the wear loss values. The study provides important insights into the resistance and wear characteristics of nano-aluminium composites, and the results can be used to improve the performance and life of these materials in various applications. Overall, this research provides a comprehensive analysis of the friction and wear behavior of nano-aluminium metal composites using machine learning through linear regression algorithm. The results obtained in this study can be used to design and optimize the production process of nano-aluminium metal composites for specific applications, leading to improved performance and longer life of the materials.

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Active Learning of Molecular data for Task-Specific Objectives

arXiv

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arXiv 2024年

作者： Ghosh, Kunal Todorović, Milica Vehtari, Aki Rinke, Patrick Department of Applied Physics Aalto University P.O. Box 11000 AaltoFI-00076 Finland Department of Computer Science Aalto University P.O. Box 15400 AaltoFI-00076 Finland Department of Mechanical and Materials Engineering University of Turku TurkuFI-20014 Finland Physics Department TUM School of Natural Sciences Technical University of Munich Garching Germany Atomistic Modelling Center Munich Data Science Institute Technical University of Munich Garching Germany

Active learning (AL) has shown promise for being a particularly data-efficient machine learning approach. Yet, its performance depends on the application and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes and GP noise settings. AL was insensitive to the acquisition batch size and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal. © 2024, CC BY-NC-ND.

关键词： Active learning

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Joint Task Scheduling and Container Image Caching in Edge Computing

Joint Task Scheduling and Container Image Caching in Edge Co...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Fangyi Mou Zhiqing Tang Jiong Lou Jianxiong Guo Wenhua Wang Tian Wang Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Institute of Artificial Intelligence and Future Networks Beijing Normal University China Yunnan Key Laboratory of Software Engineering Kunming Yunnan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Processor scheduling Computational modeling Markov decision processes Image edge detection Containers Dynamic scheduling Delays

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